使用组稀疏性进行优化对于增强机加工学习应用程序中的模型可解释性具有重要意义，例如特征选择，压缩感知和模型压缩。但是，对于大规模的随机训练问题，通常很难实现有效的小组稀疏探索。..

A Half-Space Stochastic Projected Gradient Method for Group Sparsity Regularization

Optimizing with group sparsity is significant in enhancing model interpretability in machining learning applications, e.g., feature selection, compressed sensing and model compression. However, for large-scale stochastic training problems, effective group-sparsity exploration are typically hard to achieve.Particularly, the state-of-the-art stochastic optimization algorithms usually generate merely dense solutions. To overcome this shortage, we propose a stochastic method—Half-space Stochastic Projected Gradient method (HSPG) to search solutions of high group sparsity while maintain the convergence. Initialized by a simple Prox-SG Step, the HSPG method relies on a novel Half-Space Step to substantially boosts the sparsity level. Numerically, HSPG demonstrates its superiority in deep neural networks, e.g., VGG16, ResNet18 and MobileNetV1, by computing solutions of higher group sparsity, competitive objective values and generalization accuracy.